Security & Licensing · AI Security
AI & LLM Security Assessment Services in Dubai
Test how your AI application behaves when prompts, retrieved content, identities, tools and downstream systems are deliberately misused—not only when users follow the expected path.
Dubaitech assesses the application around the model, including RAG data, agent permissions, APIs, plugins, output handling, logging and software dependencies.
Test the complete AI application
Model safeguards are only one part of application security
An AI security assessment in the UAE examines how an LLM application can expose data, mishandle untrusted content or act beyond its intended authority. The effective attack surface includes prompts, retrieval sources, session boundaries, APIs, agent tools, output consumers and third-party components.
Dubaitech uses a risk-based test plan informed by the current OWASP Top 10 for LLM and GenAI Applications and the NIST AI RMF. These references guide coverage and reporting; they do not constitute OWASP or NIST certification.
When this service is useful
Customer or employee assistants that access internal knowledge or personal data
RAG applications connected to documents, vector stores or enterprise search
AI agents allowed to call APIs, plugins, workflows or business systems
Teams preparing an AI application for pilot, production release or a material integration change
Business outcomes
Security improvements that support responsible adoption
The outcome is a practical control and operating roadmap aligned to the systems, data and decision rights in scope.
Find risk across system boundaries
Test the joins between model, application, retrieval layer, identity, tools and downstream services where controls can fail.
Use evidence, not assumptions
Document reproducible test cases, affected components, preconditions and business impact instead of relying on a generic checklist.
Prioritize practical remediation
Separate architecture, authorization, data, validation and monitoring improvements into an actionable owner-based plan.
Confirm fixes through retesting
Re-run agreed findings after remediation to verify that the observed path is addressed without claiming universal model safety.
Service capabilities
A complete review across people, process and technology
The final scope is confirmed after discovery because available telemetry, integrations, access, licensing and business authority differ by organization.
Architecture and threat modeling
Map models, trust boundaries, prompts, data stores, identities, tools and downstream actions to build a relevant test plan.
Prompt injection testing
Assess direct and indirect prompt manipulation, instruction conflicts, untrusted content and attempts to influence privileged behavior.
Sensitive-data disclosure
Test whether prompts, outputs, retrieval, errors, session handling or system design reveal data outside the intended user context.
Insecure output handling
Review how model output reaches browsers, databases, file paths, code interpreters, workflows and other downstream components.
RAG and vector security
Assess document ingestion, metadata filtering, tenant separation, retrieval authorization, poisoning exposure and source provenance.
Authentication and tenant isolation
Test session controls, object-level authorization, role enforcement and separation between users, customers or departments.
AI agent permissions
Review tool scope, delegated credentials, human approval, transaction limits and whether the agent has excessive functionality or autonomy.
APIs, plugins and integrations
Assess input validation, authorization, secrets handling, rate limits and trust assumptions across connected services.
Supply-chain risk
Review model, library, dataset, embedding, plugin and service dependencies plus provenance, update and integrity controls.
Logging and monitoring
Evaluate security-relevant events, privacy-conscious evidence, abuse signals, alerting and the ability to investigate AI activity.
Suitable use cases
Where the assessment creates practical value
Start with the real business workflow and affected information, then select controls and testing that match the risk.
Enterprise knowledge assistants
Test authorization and disclosure paths when an assistant retrieves SharePoint, file, CRM or other internal content.
Customer-facing chat and search
Review prompt abuse, session separation, output rendering, privacy controls and integrations exposed to untrusted users.
Agentic workflows
Assess agents that create records, send messages, query systems or initiate actions through APIs and tools.
Custom RAG and model applications
Evaluate ingestion, retrieval, embeddings, data ownership, model gateways and application-specific control boundaries.
Delivery process
A controlled path from discovery to improvement
Each engagement sets scope, access, responsibilities, limitations and safe operating boundaries before technical work begins.
STEP 1
Scope and rules of engagement
Confirm the application, environments, test accounts, data constraints, excluded actions and escalation contacts.
STEP 2
Architecture and threat review
Map data flow, trust boundaries, identities, tools, retrieval components, dependencies and expected business behavior.
STEP 3
Manual and structured testing
Exercise agreed abuse cases across prompts, authorization, RAG, agents, integrations, output handling and operational controls.
STEP 4
Evidence and remediation report
Document reproducible findings, risk, affected components, limitations and prioritized technical recommendations.
STEP 5
Remediation support and retest
Clarify fixes, review design changes and retest agreed findings after the application owner completes remediation.
Framework-aligned, not certified
Assessment coverage informed by OWASP GenAI and NIST AI RMF
OWASP’s 2025 LLM and GenAI risks include prompt injection, sensitive-information disclosure, supply-chain weaknesses, improper output handling, excessive agency and vector or embedding weaknesses. Dubaitech uses relevant categories to inform testing rather than treating them as a fixed certification checklist.
NIST AI RMF is a voluntary framework organized around governing, mapping, measuring and managing AI risk. Testing scope is adapted to the application, available access and business impact. Privacy-sensitive production data is avoided or minimized wherever practical, with test evidence handled under the agreed engagement rules.
Controls considered in the engagement
This content is general technical information, not legal advice or a promise of compliance or certification. Customers remain responsible for obtaining advice and approvals appropriate to their organization, sector and intended use.
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Questions answered
Frequently asked questions
Start with an assessment
Book an AI Application Security Review
Share the application purpose, architecture, deployment stage and connected data or tools. Dubaitech will define a safe review scope and an evidence-led test plan for the complete AI application.